{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tensorflow_homework_senior.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/henrygas/tensorflow_2_learn/blob/master/tensorflow_homework_senior.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vS1H2AYVLBKB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "9333a3ac-65ee-4969-b194-b694cbcbba4f"
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "tf.logging.set_verbosity(tf.logging.INFO)\n",
        "print(tf.__version__)\n",
        "print(np.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.15.0-rc3\n",
            "1.16.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuMn58zLL-zI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "f8ebee2a-7242-4354-87a3-310c85b53746"
      },
      "source": [
        "mnist = input_data.read_data_sets(\"./\")\n",
        "\n",
        "print(mnist.train.images.shape)\n",
        "print(mnist.train.labels.shape)\n",
        "\n",
        "print(mnist.validation.images.shape)\n",
        "print(mnist.validation.labels.shape)\n",
        "\n",
        "print(mnist.test.images.shape)\n",
        "print(mnist.test.labels.shape)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-2-b0ffac263758>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please write your own downloading logic.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use urllib or similar directly.\n",
            "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting ./train-images-idx3-ubyte.gz\n",
            "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting ./train-labels-idx1-ubyte.gz\n",
            "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
            "Extracting ./t10k-images-idx3-ubyte.gz\n",
            "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
            "Extracting ./t10k-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
            "(55000, 784)\n",
            "(55000,)\n",
            "(5000, 784)\n",
            "(5000,)\n",
            "(10000, 784)\n",
            "(10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UENoVXIIMy5E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "f1036757-6194-49f5-a778-b222a85b8cdf"
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "for i in range(16):\n",
        "  plt.subplot(4, 4, i + 1)\n",
        "  plt.axis(\"off\")\n",
        "  plt.title(\"[{}]\".format(mnist.train.labels[i]))\n",
        "  plt.imshow(mnist.train.images[i].reshape((28, 28)))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yT+vqlK/qObHQdu+v6xL2k+Qv1+NOFwAAT0i6AAB4Uq6Gl/+8rJOJ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\nnpB0AQDwhKQLAIAnJF0AADz5f3XpyC6H9WCIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQElac9YNTfN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "de605e6b-b659-48d3-d4a8-babec4c2ed69"
      },
      "source": [
        "x = tf.placeholder(\"float\", [None, 784])\n",
        "y = tf.placeholder(\"int64\", [None])\n",
        "learning_rate = tf.placeholder(\"float\")\n",
        "\n",
        "def initialize(shape, stddev=0.1):\n",
        "  return tf.truncated_normal(shape, stddev=stddev)\n",
        "\n",
        "L1_units_count = 100\n",
        "W_1 = tf.Variable(initialize([784, L1_units_count]))\n",
        "b_1 = tf.Variable(initialize([L1_units_count]))\n",
        "logits_1 = tf.matmul(x, W_1) + b_1\n",
        "output_1 = tf.nn.relu(logits_1)\n",
        "\n",
        "L2_units_count = 100\n",
        "W_2 = tf.Variable(initialize([L1_units_count, L2_units_count]))\n",
        "b_2 = tf.Variable(initialize([L2_units_count]))\n",
        "logits_2 = tf.matmul(output_1, W_2) + b_2\n",
        "output_2 = tf.nn.relu(logits_2)\n",
        "\n",
        "L3_units_count = 10\n",
        "W_3 = tf.Variable(initialize([L2_units_count, L3_units_count]))\n",
        "b_3 = tf.Variable(initialize([L3_units_count]))\n",
        "logits_3 = tf.matmul(output_2, W_3) + b_3\n",
        "\n",
        "logits = logits_3\n",
        "\n",
        "cross_entropy_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
        "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cross_entropy_loss)\n",
        "\n",
        "pred = tf.nn.softmax(logits)\n",
        "correct_pred = tf.equal(tf.argmax(pred, 1), y)\n",
        "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
        "\n",
        "batch_size = 32\n",
        "max_train_step = 1000\n",
        "learning_rate_value = 0.3\n",
        "\n",
        "saver = tf.train.Saver()\n",
        "with tf.Session() as ses:\n",
        "  ses.run(tf.global_variables_initializer())\n",
        "\n",
        "  validation_data = {\n",
        "      x: mnist.validation.images,\n",
        "      y: mnist.validation.labels,\n",
        "  }\n",
        "\n",
        "  test_data = {\n",
        "      x: mnist.test.images,\n",
        "      y: mnist.test.labels\n",
        "  }\n",
        "\n",
        "  for i in range(max_train_step):\n",
        "    xs, ys = mnist.train.next_batch(batch_size)\n",
        "    _, loss = ses.run(\n",
        "        [optimizer, cross_entropy_loss],\n",
        "        feed_dict={\n",
        "            x: xs,\n",
        "            y: ys,\n",
        "            learning_rate: learning_rate_value\n",
        "        })\n",
        "    \n",
        "    if i > 0 and i % 100 == 0:\n",
        "      validation_accuracy = ses.run(accuracy, feed_dict=validation_data)\n",
        "      print(\"after {} training steps, the loss is:{}, validation_accuracy is:{}\".format(\n",
        "          i + 1, loss, validation_accuracy))\n",
        "      \n",
        "      saver.save(ses, \"./model.ckpt\", global_step=i)\n",
        "\n",
        "  print(\"The training has finished!\")\n",
        "  acc_train = ses.run(accuracy, feed_dict={\n",
        "      x: mnist.train.images,\n",
        "      y: mnist.train.labels\n",
        "  })\n",
        "  print(\"The train accuracy is: {}\".format(acc_train))\n",
        "  acc_validation = ses.run(accuracy, feed_dict=validation_data)\n",
        "  print(\"The validation accuracy is: {}\".format(acc_validation))\n",
        "  acc_test = ses.run(accuracy, feed_dict=test_data)\n",
        "  print(\"The test accuracy is: {}\".format(acc_test))\n",
        "\n",
        "rows = 7\n",
        "cols = 8\n",
        "with tf.Session() as ses:\n",
        "  ckpt = tf.train.get_checkpoint_state(\"./\")\n",
        "  if ckpt and ckpt.model_checkpoint_path:\n",
        "    saver.restore(ses, ckpt.model_checkpoint_path)\n",
        "\n",
        "    show_size = rows * cols\n",
        "\n",
        "    final_pred, acc = ses.run(\n",
        "        [pred, accuracy],\n",
        "        feed_dict={\n",
        "            x: mnist.test.images[:show_size],\n",
        "            y: mnist.test.labels[:show_size]\n",
        "        })\n",
        "    \n",
        "    print(acc)\n",
        "\n",
        "    orders = np.argsort(final_pred)\n",
        "\n",
        "    plt.figure(figsize=(2*cols, 2*rows))\n",
        "    for idx in range(show_size):\n",
        "      order = orders[idx, :][-1]\n",
        "      prob = final_pred[idx, :][order]\n",
        "\n",
        "      plt.subplot(rows, cols, idx + 1)\n",
        "      plt.axis(\"off\")\n",
        "      plt.title(\"{}, [{}-{:.1f}%]\".format(mnist.test.labels[idx], order, prob * 100))\n",
        "      plt.imshow(mnist.test.images[idx].reshape((28, 28)))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "after 101 training steps, the loss is:0.24815191328525543, validation_accuracy is:0.8948000073432922\n",
            "after 201 training steps, the loss is:0.14328226447105408, validation_accuracy is:0.9161999821662903\n",
            "after 301 training steps, the loss is:0.4998488128185272, validation_accuracy is:0.9186000227928162\n",
            "after 401 training steps, the loss is:0.4671897888183594, validation_accuracy is:0.9088000059127808\n",
            "after 501 training steps, the loss is:0.10365037620067596, validation_accuracy is:0.9440000057220459\n",
            "after 601 training steps, the loss is:0.48984667658805847, validation_accuracy is:0.9422000050544739\n",
            "after 701 training steps, the loss is:0.7058823108673096, validation_accuracy is:0.8870000243186951\n",
            "after 801 training steps, the loss is:0.15038448572158813, validation_accuracy is:0.9567999839782715\n",
            "after 901 training steps, the loss is:0.13812261819839478, validation_accuracy is:0.9472000002861023\n",
            "The training has finished!\n",
            "The train accuracy is: 0.9488000273704529\n",
            "The validation accuracy is: 0.9503999948501587\n",
            "The test accuracy is: 0.9473000168800354\n",
            "INFO:tensorflow:Restoring parameters from ./model.ckpt-900\n",
            "0.96428573\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3Zbwn7NiG/HLJcjwahZ2LRE+jriTKWaWVHgs6H8AI7/U8DfoDalKkbUSku+gp/OtBt9MH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bctAIaGlL0COrW0xNswj//tiwDeDPN6Q+//kJZHm/zr+xqpvgh1POn933Z621fzkPkDACzxttll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\nRERE5Iv/B4p/eYyd6R06AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x1008 with 56 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}